PHYSICAL ANALYSIS OF SOCIAL DYNAMICS: A SOCIOPHYSICS PERSPECTIVE

Abstract

Sociophysics is an interdisciplinary field that uses methods from the physical sciences to study human behavior and interactions. It includes mathematical and computational techniques such as big data analysis, statistical modeling, network theory, and simulations. It analyzes complex systems to understand the dynamics of society. Historically, sociophysics emerged from applying statistical mechanics and thermodynamics to social phenomena. Foundational work has modeled opinion dynamics, crowd behavior, and information diffusion in social networks, and provided insights into consensus, polarization, and social stability. The basic concepts are based on treating individuals as agents, using network theory, and applying statistical mechanics and dynamical systems. Methods include big data analysis, statistical modeling, simulations, and network analysis. Future research will aim to integrate artificial intelligence and machine learning, foster interdisciplinary collaboration, utilize real-time data, and apply findings to public policy. Sociophysics aims to improve our understanding of social systems and solve today's society's complex problems. In this study, the field of sociophysics, its historical development, studies in the literature, and methods were generally discussed.

Keywords

• Complex systems
• dynamics
• sociology
• sociophysics

References

1. Dautenhahn, K. (1997). I could be you: The phenomenological dimension of social understanding. Cybernetics & Systems, 28(5), 417-453.
2. Bhat, R. M., Silllalee, A., & Kandasamy, L. S. (2023). Concepts and Contexts: The Interplay of Philosophy and History in Understanding Human Society. East Asian Journal of Multidisciplinary Research, 2(6), 2581-2590.
3. Kunisch, S., Denyer, D., Bartunek, J. M., Menz, M., & Cardinal, L. B. (2023). Review research as scientific inquiry. Organizational Research Methods, 26(1), 3-45.
4. Neumann, E., & Zaki, J. (2023). Toward a social psychology of cynicism. Trends in Cognitive Sciences, 27(1), 1-3.
5. Gaffal, M., & Padilla Gálvez, J. (2024). Negotiation, Game Theory and Language Games. In Dynamics of Rational Negotiation: Game Theory, Language Games and Forms of Life (pp. 11-40). Cham: Springer Nature Switzerland.
6. Gilleard, J., & Gilleard, J. D. (2002). Developing cross-cultural communication skills. Journal of professional issues in engineering education and practice, 128(4), 187-200.
7. Stetsenko, A. (2005). Activity as object-related: Resolving the dichotomy of individual and collective planes of activity. Mind, culture, and activity, 12(1), 70-88.
8. Saeverot, H. (2024). Hegel’s Phenomenology of Spirit as Bildungsroman. Studies in Philosophy and Education, 43(1), 1-13.

9. Mayrl, D., & Wilson, N. H. (2024). Comparison after Positivism. In After Positivism: New Approaches to Comparison in Historical Sociology (pp. 1-26). Columbia University Press.
10. Brian, É. (2024). Analytical Probability, Averages and Data Distributions in the 19th Century. In Are Statistics Only Made of Data? Know-how and Presupposition from the 17th and 19th Centuries (pp. 71-144). Cham: Springer International Publishing.
11. François, K., & Monteiro, C. (2023). Reflections on Civic Statistics: A Triangulation of Citizen, State and Statistics: Past, Present and Future. In Statistics for Empowerment and Social Engagement: teaching Civic Statistics to develop informed citizens (pp. 505-536). Cham: Springer International Publishing.
12. Davis, P. J. (2023). Entropy and society: can the physical/mathematical notions of entropy be usefully imported into the social sphere?. In Frontiers in Entropy Across the Disciplines: Panorama of Entropy: Theory, Computation, and Applications (pp. 1-18).
13. Emmeche, C. (2023). At home in a complex world: Lessons from the frontiers of natural science. In The Significance of Complexity (pp. 21-46). Routledge.
14. Schweitzer, F. (2018). Sociophysics. Physics today, 71(2), 40-46.
15. Sen, P., & Chakrabarti, B. K. (2014). Sociophysics: an introduction. OUP Oxford.
16. Barnes, T. J., & Wilson, M. W. (2014). Big data, social physics, and spatial analysis: The early years. Big Data & Society, 1(1), 2053951714535365.
17. Mandel, I., & Kuznetsov, D. V. (2009). Statistical and physical paradigms in the social sciences. Model Assisted Statistics and Applications, 4(1), 39-62.
18. Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., ... & Perc, M. (2022). Social physics. Physics Reports, 948, 1-148.
19. Urry, J. (2004). Small worlds and the new ‘social physics’. Global networks, 4(2), 109-130.
20. Mirowski, P. (1991). More heat than light: economics as social physics, physics as nature's economics. Cambridge University Press.
21. Bhattacharya, K., & Kaski, K. (2019). Social physics: uncovering human behaviour from communication. Advances in Physics: X, 4(1), 1527723.
22. Yukalov, V. I. (2023). Selected topics of social physics: Equilibrium systems. Physics, 5(2), 590-635.
23. Adolf, M. T., & Stehr, N. (2018). Information, knowledge, and the return of social physics. Administration & Society, 50(9), 1238-1258.
24. Robertson, George Croom (1911). "Hobbes, Thomas" . Encyclopædia Britannica. Vol. 13 (11th ed.). pp. 545–55
25. Balz, A. G. (1937). The Challenge of Metaphysics to Social Science. J. Soc. Phil., 3, 101.
26. Iggers, G. G. (1959). Further Remarks about Early Uses of the Term" Social Science". Journal of the History of Ideas, 433-436.
27. Senn, P. (2000). Mathematics and the social sciences at the time of the modern beginnings of the social sciences. Journal of Economic Studies, 27(4/5), 271-292.
28. François, K., & Bracke, N. (2006). Teaching statistics in a critical way: Historical, philosophical and political aspects of statistics. In 7th International Conference on Teaching Statistics (ICOTS 7). International Association for Statistical Education.
29. Kuijper, H. (2022). The Concept of Country. In Comprehending the Complexity of Countries: The Way Ahead (pp. 55-88). Singapore: Springer Nature Singapore.
30. Kleingeld, P. (2017). Contradiction and Kant’s formula of universal law. Kant-Studien, 108(1), 89-115.
31. Kuusela, V. (2012). Laplace-a pioneer of statistical inference. J. Électron. Hist. Probab. Stat, 8, 1-24.
32. Britannica, T. Editors of Encyclopaedia (2024, April 5). Henri de Saint-Simon. Encyclopedia Britannica. https://www.britannica.com/biography/Henri-de-Saint-Simon
33. J. H. Goldthorpe, “Quetelet and his critics”, in Pioneers of sociological science: statistical foundations and the theory of action (Cambridge University Press, 2021), pp. 25–41.
34. Perc, M. (2019). The social physics collective. Scientific reports, 9(1), 16549.
35. Ball, P. (2002). The physical modelling of society: a historical perspective. Physica A: Statistical Mechanics and its Applications, 314(1-4), 1-14.
36. Jahoda, G. (2015). Quetelet and the emergence of the behavioral sciences. Springerplus, 4, 1-10.
37. Garry W. Trompf, Encyclopedia of Knowledge Organization, edited by Birger Hjørland and Claudio Gnoli, ; https://www.isko.org/cyclo/comte#1.4
38. Stauffer, D. (2013). A biased review of sociophysics. Journal of Statistical Physics, 151, 9-20.
39. Deltete, R. J. (2012). Josiah Willard Gibbs (1839-1903). In Philosophy of Chemistry (pp. 89-100). North-Holland.
40. Mohanty, R. K. (2023). Comparative History in Sociological Writings of Max Weber. Sociological Bulletin, 72(1), 56-72.
41. Batty, M. (2023). A new kind of search. Environment and Planning B: Urban Analytics and City Science, 50(3), 575-578.
42. Rousseau, R. (2002). George Kingsley Zipf: life, ideas, his law and informetrics. Glottometrics, 3(1), 11-18.
43. Barnes, T. J., & Wilson, M. W. (2014). Big data, social physics, and spatial analysis: The early years. Big Data & Society, 1(1), 2053951714535365.
44. Bassani, G. F. (Ed.). (2007). Ettore Majorana: Scientific Papers. Springer Science & Business Media.
45. Chakraborti, A., Raina, D., & Sharma, K. (2016). Can an interdisciplinary field contribute to one of the parent disciplines from which it emerged?. The European Physical Journal Special Topics, 225, 3127-3135.
46. Stewart, J. Q. (1947). Empirical mathematical rules concerning the distribution and equilibrium of population. Geographical review, 37(3), 461-485.
47. Stewart, J. Q. (1948). Demographic gravitation: evidence and applications. Sociometry, 11(1/2), 31-58.
48. Nicholas Rashevsky Mathematical Theory of Human Relations: An Approach to Mathematical Biology of Social Phenomena. Bloomington, ID: Principia Press, 1947/1949 (2nd ed.)
49. Outline of a Unified Approach to Physics, Biology and Sociology., Bulletin of Mathematical Biophysics 31 (1969): 159–198. Outline of a Mathematical Theory of Human Relations Author(s): N. Rashevsky Source: Philosophy of Science , Oct., 1935, Vol. 2, No. 4 (Oct., 1935), pp. 413-430
50. Likert, R. (1947). Kurt Lewin: A pioneer in human relations research. Human Relations, 1(1), 131-140.
51. Holton, G. (2004). Robert K. Merton. Proceedings of the American Philosophical Society, 148(4), 505.
52. Wiener, N. (1938). The homogeneous chaos. American Journal of Mathematics, 60(4), 897-936.
53. Masani, P. R. (2012). Norbert Wiener 1894–1964 (Vol. 5). Birkhäuser.
54. Jeřábek, H. (2001). Paul Lazarsfeld—The founder of modern empirical sociology: A research biography. International journal of public opinion research, 13(3), 229-244.
55. Rapoport, A. (1960). Fights, games, and debates. University of Michigan Press.
56. Solomonoff, R., & Rapoport, A. (1951). Connectivity of random nets. The bulletin of mathematical biophysics, 13, 107-117.
57. Schelling T.C. Dynamic models of segregation J. Math. Sociol., 1 (1971), pp. 143-186
58. Schelling, T. C. (1992). Some economics of global warming. The American Economic Review, 82(1), 1-14.
59. Arrow, K. J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J. O., ... & Zitzewitz, E. (2008). The promise of prediction markets. Science, 320(5878), 877-878.
60. Galam, S., & Galam, S. (2012). What is sociophysics about? (pp. 3-19). Springer US.
61. Galam, S. (2016). Stubbornness as an unfortunate key to win a public debate: an illustration from sociophysics. Mind & Society, 15, 117-130.
62. Galam, S. (2017). The Trump phenomenon: An explanation from sociophysics. International Journal of Modern Physics B, 31(10), 1742015.
63. Mantegna, R. N., & Stanley, H. E. (1999). Introduction to econophysics: correlations and complexity in finance. Cambridge university press.
64. Lillo, F., Farmer, J. D., & Mantegna, R. N. (2003). Master curve for price-impact function. Nature, 421(6919), 129-130.
65. Glymour, C. (1983). Social science and social physics. Behavioral Science, 28(2), 126-134.
66. Savoiu, G., & Siman, I. I. (2012). Sociophysics: A new science or a new domain for physicists in a modern university. Econophysics: Background and applications in economics, finance, and sociophysics, 149-168.
67. Drye, T. (2016). Sociophysics: A framework to identify transitions in collective supporter behaviour. Journal of Direct, Data and Digital Marketing Practice, 17, 252-257.
68. Ghosh, A., Monsivais, D., Bhattacharya, K., & Kaski, K. (2017). Social Physics: Understanding Human Sociality in Communication Networks. Econophysics and Sociophysics: Recent Progress and Future Directions, 187-200.
69. Kaufman, S., Kaufman, M., & Diep, H. T. (2018). Sociophysics of social conflict. Physics Today, 71(8), 12-13.
70. Capraro, V., & Perc, M. (2018). Grand challenges in social physics: in pursuit of moral behavior. Frontiers in Physics, 6, 107.
71. Bhattacharya, K., & Kaski, K. (2019). Social physics: uncovering human behaviour from communication. Advances in Physics: X, 4(1), 1527723.
72. Kaufman, M., Diep, H. T., & Kaufman, S. (2020). Sociophysics analysis of multi-group conflicts. Entropy, 22(2), 214.
73. Ellero, A., Fasano, G., & Favaretto, D. (2020). An application of Linear Programming to sociophysics models. In ceur workshop proceedings (Vol. 2795, pp. 23-33). Alexander Shapoval, Victor Popov.
74. Ishii, A., & Okano, N. (2021). Sociophysics approach of simulation of mass media effects in society using new opinion dynamics. In Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 3 (pp. 13-28). Springer International Publishing.
75. Tanimoto, J. (2021). Sociophysics approach to epidemics (Vol. 23). Singapore: Springer.
76. Kutner, R. (2022). Econophysics and sociophysics: their milestones & challenges. Part 2. Postępy Fizyki, 73.
77. Dil, E., & Dil, E. (2023). Sociophysics of income distributions modeled by deformed fermi-dirac distributions. The Journal of Mathematical Sociology, 47(2), 97-122.
78. Tsintsaris, D., Tsompanoglou, M., & Ioannidis, E. (2024). Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business. Mathematics, 12(8), 1141.
79. Chakrabarti, B. K., Chakraborti, A., & Chatterjee, A. (Eds.). (2006). Econophysics and sociophysics: trends and perspectives.
80. Breiger, R. L. (2004). The analysis of social networks. Handbook of data analysis, 505-526. Krause, J., Croft, D. P., & James, R. (2007). Social network theory in the behavioural sciences: potential applications. Behavioral Ecology and Sociobiology, 62, 15-27.
81. Maksymov, I. S., & Pogrebna, G. (2024). Quantum-Mechanical Modelling of Asymmetric Opinion Polarisation in Social Networks. Information, 15(3), 170.
82. Liao, J., & Yang, X. (2024). Kinetic modeling of a Sznajd opinion model on social networks. International Journal of Modern Physics C.
83. Oliveira, I., Wang, C., Dong, G., & Vilela, A. L. (2024). Opinion Dynamics Entropy Generation via Complex Network Structures. Bulletin of the American Physical Society.
84. Helbing, D. (2012). Agent-based modeling. In Social self-organization: Agent-based simulations and experiments to study emergent social behavior (pp. 25-70). Berlin, Heidelberg: Springer Berlin Heidelberg.
85. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual review of sociology, 28(1), 143-166.
86. Getchell, A. (2008). Agent-based modeling. Physics, 22(6), 757-767.
87. Stauffer, D. (2002). Sociophysics: the Sznajd model and its applications. Computer physics communications, 146(1), 93-98.
88. Crokidakis, N. (2012). Effects of mass media on opinion spreading in the Sznajd sociophysics model. Physica A: Statistical Mechanics and its Applications, 391(4), 1729-1734.
89. Tanimoto, J. (2019). Evolutionary games with sociophysics. Evolutionary Economics, 17.
90. Basu, B., Chakrabarti, B. K., Chakravarty, S. R., & Gangopadhyay, K. (Eds.). (2010). Econophysics and economics of games, social choices and quantitative techniques. Milan: Springer.
91. Sánchez, A. (2018). Physics of human cooperation: experimental evidence and theoretical models. Journal of Statistical Mechanics: Theory and Experiment, 2018(2), 024001.
92. Thovex, C., & Trichet, F. (2013). Semantic social networks analysis: Towards a sociophysical knowledge analysis. Social Network Analysis and Mining, 3, 35-49.
93. Sobkowicz, P. (2019). Social simulation models at the ethical crossroads. Science and Engineering Ethics, 25(1), 143-157.
94. Helbing, D., Balietti, S., Bishop, S., & Lukowicz, P. (2011). Understanding, creating, and managing complex techno-socio-economic systems: Challenges and perspectives. The European Physical Journal Special Topics, 195(1), 165-186.
95. Kutner, R., Ausloos, M., Grech, D., Di Matteo, T., Schinckus, C., & Stanley, H. E. (2019). Econophysics and sociophysics: Their milestones & challenges. Physica A: Statistical Mechanics and its Applications, 516, 240-253.
96. Kawahata, Y., Okano, N., Higashi, M., Wakabayashi, T., & Ishii, A. (2018, December). The Influence of Social Media Writing on Online Search Behavior for Seasonal Topics: The Sociophysics Approach. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4339-4345). IEEE.
97. Vicsek, T., & Zafeiris, A. (2012). Collective motion. Physics reports, 517(3-4), 71-140.
98. Schadschneider, A. (1999). The nagel-schreckenberg model revisited. The European Physical Journal B-Condensed Matter and Complex Systems, 10, 573-582.
99. Schadschneider, A., Chowdhury, D., & Nishinari, K. (2010). Stochastic transport in complex systems: from molecules to vehicles. Elsevier.
100. Alodjants, A. P., Bazhenov, A. Y., Khrennikov, A. Y., & Bukhanovsky, A. V. (2022). Mean-field theory of social laser. Scientific Reports, 12(1), 8566.